Abdominal aortic aneurysms (AAA) remain one of the largest preventable causes of death (>15,000/yr.) in the US. AAA rupture is the result of the material failure of the aortic wall. The primary diagnostic knowledge gap is an inability to predic risk of AAA rupture based on aortic wall geometric characteristics. Aneurysmal diameter is the current criteria to determine repair but many AAA rupture-related deaths occurs at smaller sizes or remain stable at larger sizes. The goal of this project is to develop a novel, transcutaneous imaging modality to predict the risk of AAA rupture utilizing an approach incorporating concepts of material failure with novel ultrasound stress measurement algorithms. This work is the continuation of prior funding granted by the University of Rochester's Clinical and Translational Science Institute. A novel stress measurement algorithm will be developed for use with an Ultrasonix SonixTouch ultrasound system to measure deformation and model the vessel tissue over an entire cardiac cycle. This algorithm will be tested and validated using computational simulations and tissue mimicking, ultrasound phantoms. Mechanical modeling of AAA and image simulation algorithms will be used to develop, and quantify the accuracy of, our algorithm. In addition, geometrically complex phantoms will be used to further validate our algorithm in near in vivo conditions. These phantoms will be constructed using a process previously developed by our group using segmented X-ray CT images and 3D printing technology to create anatomically accurate phantoms. These phantoms will also be tested on a hemodynamic simulator under physiologic pressure conditions. Clinical efficacy of our algorithm will also be tested using a small patient population, diagnosed with AAA and undergoing clinical observation. The ultimate goal of this project is to continue the validation of this technique to a larger clinical study.